The Institute of Crypto-Assets (Institut des Crypto-Actifs) is a pioneering initiative of the Léonard de Vinci Engineering School (ESILV), a five-year engineering school located in Paris, France. It is fully accredited to award the title of "ingénieur" by the French Commission of Engineering Titles and it is one of France's top schools. ESILV is part of the "Pole Universitaire Léonard de Vinci". The director of the school is Mr Pascal Pinot. The ESILV is a pioneer in the teaching and development of technologies at the intersection of finance, mathematics and computer science. In fact, since 2016, ESILV has been the first school to offer blockchain certification of diplomas. It is also the first engineering school in Europe to offer courses on bitcoin and the lightning network (a technology capable of increasing the capacity of the Bitcoin network). Finally, ESILV is a forerunner in the field of finance, with the creation of a major entirely dedicated to Fintech, headed by Cyril Grunspan, director of the Institut des Crypto-Actifs, which aims to combine research, education and partnerships with companies to demystify the world of crypto finance.

Postdoc position on Crypto Futures and Market making, supervised by Martino Grasselli (ESILV and Univ. Padua), the scientific director of the Institute. The project will be carried out within a hedge fund in the Paris region.  Remuneration and job perspectives are attractive compared to similar positions.

The roadmap of the project is supposed to be as follows:

• Automatize calibration of parameters for market making algorithm à la “Avellaneda & Stoikov” (Market making under inventory risks, Quantitative Finance 2008) in the cryptocurrencies market.
• We have identified some mid-frequency alphas on main crypto futures. Validate/Challenge/Improve our approach for incorporating alphas into market making strategies.
• Build relevant features for microstructure alpha on perpetual futures.
• Build an API that feeds trading automaton with calibrated parameters and real time alphas.

Applicants must hold (or are going to finish) a Ph.D. in financial engineering or computer programming and have an active record of research and publication. Qualifications must include a deep knowledge of financial derivatives and option pricing/volatility modeling issues.

The following hard skills are more than welcome:

• a strong understanding of market microstructure /optimal execution research topics;
• experimented in Time series analysis/supervised Machine Learning;
• master Python and its libraries, writing clear, modular, and maintainable code: we want teammates who believe in the efficiency of readable code.

Personal profile of the candidate and soft skills:

• need to be biased towards excellence, be radically open minded to new ideas and ways of reaching higher standards. As a team player, he/she will search consistently to get in sync with the team;
• hands-on and pragmatic person and has both a business-focus mindset;
• comfortable with strongly processed approaches which are key for building efficient research pipelines.